Adversarial Label-Efficient Satellite Image Change Detection

Satellite image change detection aims at finding occurrences of targeted changes in a given scene taken at different instants. This task is highly challenging due to the acquisition conditions and also to the subjectivity of changes. In this paper, we investigate satellite image change detection using active learning. Our method is interactive and relies on a question & answer model which asks the oracle (user) questions about the most informative display (dubbed as virtual exemplars), and according to the user’s responses, updates change detections. The main contribution of our method consists in a novel adversarial model that allows frugally probing the oracle only with the most representative, diverse and uncertain virtual exemplars. The latter are learned to challenge (the most) the trained change decision criteria which ultimately leads to a better re-estimate of these criteria in the following iterations of active learning. Conducted experiments show the out-performance of our proposed adversarial display model against related display strategies as well as the related work.